Uranium accumulation and toxicity in the green alga <i>Chlamydomonas reinhardtii</i> is modulated by pH
Bibliographic record
Abstract
The effects of pH on metal uptake and toxicity in aquatic organisms are currently poorly understood and remain an evolving topic in studies about the biotic ligand model (BLM). In the present study, the authors investigated how pH may influence long-term (4 d) uranium (U) accumulation and chronic toxicity in batch cultures of the freshwater green alga Chlamydomonas reinhardtii. The toxicity expressed as a function of the free uranyl ion was much greater at pH 7 (effective concentration, 50% [EC50] = 1.8 × 10(-9) M UO2 (2+) ) than at pH 5 (EC50 = 1.2 × 10(-7) M UO2 (2+) ). The net accumulation rate of U in algal cells was much higher at pH 7 than at pH 5 for the same free [UO2 (2+) ], but the cells exposed at pH 5 were also more sensitive to intracellular U than the cells at pH 7 with EC50s of 4.0 × 10(-15) and 7.1 × 10(-13) mol of internalized U cell(-1) , respectively. The higher cellular sensitivity to U at pH 5 than at pH 7 could be explained partly by the increase in cytosolic U binding to algal soluble proteins or enzymes at pH 5 as observed by subcellular fractionation. To predict U accumulation and toxicity in algae accurately, the important modulating effects of pH on U accumulation and U cellular sensitivity should be considered in the BLM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".